Netflix_2015_logo.svg.webp

In [1]:
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go

import plotly.io as pio
pio.renderers.default='notebook'
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
netflix_overall = pd.read_csv("netflix_titles.csv")
netflix_overall.head()
Out[2]:
show_id type title director cast country date_added release_year rating duration listed_in description
0 s1 TV Show 3% NaN João Miguel, Bianca Comparato, Michel Gomes, R... Brazil August 14, 2020 2020 TV-MA 4 Seasons International TV Shows, TV Dramas, TV Sci-Fi &... In a future where the elite inhabit an island ...
1 s2 Movie 7:19 Jorge Michel Grau Demián Bichir, Héctor Bonilla, Oscar Serrano, ... Mexico December 23, 2016 2016 TV-MA 93 min Dramas, International Movies After a devastating earthquake hits Mexico Cit...
2 s3 Movie 23:59 Gilbert Chan Tedd Chan, Stella Chung, Henley Hii, Lawrence ... Singapore December 20, 2018 2011 R 78 min Horror Movies, International Movies When an army recruit is found dead, his fellow...
3 s4 Movie 9 Shane Acker Elijah Wood, John C. Reilly, Jennifer Connelly... United States November 16, 2017 2009 PG-13 80 min Action & Adventure, Independent Movies, Sci-Fi... In a postapocalyptic world, rag-doll robots hi...
4 s5 Movie 21 Robert Luketic Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar... United States January 1, 2020 2008 PG-13 123 min Dramas A brilliant group of students become card-coun...

TV Show vs Movie¶

In [3]:
netflix_shows=netflix_overall[netflix_overall['type']=='TV Show']
netflix_movies=netflix_overall[netflix_overall['type']=='Movie']

sns.set(style="darkgrid")
ax = sns.countplot(x="type", data=netflix_overall, hue='type', palette="Set2")
No description has been provided for this image

Monthly Content Update over Years¶

https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html#sphx-glr-gallery-images-contours-and-fields-image-annotated-heatmap-py

In [4]:
netflix_date = netflix_shows[['date_added']].dropna()
netflix_date['year'] = netflix_date['date_added'].apply(lambda x : x.split(', ')[-1])
netflix_date['month'] = netflix_date['date_added'].apply(lambda x : x.lstrip().split(' ')[0])

month_order = ['January', 'February', 'March', 'April', 'May', 'June', 
               'July', 'August', 'September', 'October', 'November', 'December'][::-1]

monthly_content = netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
monthly_content
Out[4]:
year 2008 2013 2014 2015 2016 2017 2018 2019 2020 2021
month
December 0.0 0.0 1.0 7.0 44.0 39.0 64.0 50.0 74.0 0.0
November 0.0 0.0 3.0 2.0 18.0 31.0 41.0 77.0 55.0 0.0
October 0.0 2.0 0.0 5.0 18.0 32.0 46.0 73.0 56.0 0.0
September 0.0 1.0 0.0 1.0 19.0 33.0 44.0 44.0 62.0 0.0
August 0.0 1.0 0.0 0.0 17.0 38.0 34.0 53.0 61.0 0.0
July 0.0 0.0 0.0 3.0 10.0 34.0 30.0 67.0 53.0 0.0
June 0.0 0.0 0.0 3.0 8.0 30.0 28.0 48.0 48.0 0.0
May 0.0 0.0 0.0 2.0 4.0 25.0 28.0 49.0 64.0 0.0
April 0.0 0.0 1.0 4.0 8.0 29.0 31.0 50.0 58.0 0.0
March 0.0 1.0 0.0 2.0 3.0 38.0 38.0 60.0 56.0 0.0
February 1.0 0.0 1.0 1.0 7.0 18.0 24.0 46.0 46.0 0.0
January 0.0 0.0 0.0 0.0 29.0 14.0 22.0 39.0 64.0 29.0
In [5]:
monthly_content.to_numpy()
Out[5]:
array([[ 0.,  0.,  1.,  7., 44., 39., 64., 50., 74.,  0.],
       [ 0.,  0.,  3.,  2., 18., 31., 41., 77., 55.,  0.],
       [ 0.,  2.,  0.,  5., 18., 32., 46., 73., 56.,  0.],
       [ 0.,  1.,  0.,  1., 19., 33., 44., 44., 62.,  0.],
       [ 0.,  1.,  0.,  0., 17., 38., 34., 53., 61.,  0.],
       [ 0.,  0.,  0.,  3., 10., 34., 30., 67., 53.,  0.],
       [ 0.,  0.,  0.,  3.,  8., 30., 28., 48., 48.,  0.],
       [ 0.,  0.,  0.,  2.,  4., 25., 28., 49., 64.,  0.],
       [ 0.,  0.,  1.,  4.,  8., 29., 31., 50., 58.,  0.],
       [ 0.,  1.,  0.,  2.,  3., 38., 38., 60., 56.,  0.],
       [ 1.,  0.,  1.,  1.,  7., 18., 24., 46., 46.,  0.],
       [ 0.,  0.,  0.,  0., 29., 14., 22., 39., 64., 29.]])
In [6]:
monthly_content.shape
Out[6]:
(12, 10)
In [7]:
range(monthly_content.shape[1])
Out[7]:
range(0, 10)
In [8]:
plt.figure(figsize=(10, 7), dpi=200)
plt.pcolor(monthly_content, cmap='afmhot_r', edgecolors='white', linewidths=2) # heatmap

plt.xticks(np.arange(0.5, len(monthly_content.columns), 1), monthly_content.columns, fontsize=7, fontfamily='serif')
plt.yticks(np.arange(0.5, len(monthly_content.index), 1), monthly_content.index, fontsize=7, fontfamily='serif')

plt.title('Netflix Contents Update', fontsize=12, fontweight='bold', position=(0.20, 1.0+0.02))
cbar = plt.colorbar()

cbar.ax.tick_params(labelsize=8) 
cbar.ax.minorticks_on()


plt.show()
No description has been provided for this image

Text properties and layout¶

https://matplotlib.org/stable/users/explain/text/text_props.html

In [9]:
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 
               'July', 'August', 'September', 'October', 'November', 'December']

monthly_content = netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
monthly_content

fig, ax = plt.subplots(figsize=(10, 7), dpi=200)
im = ax.imshow(monthly_content, cmap='afmhot_r')

# Loop over data dimensions and create text annotations.
for i in range(monthly_content.shape[0]):  # loop columns
    for j in range(monthly_content.shape[1]):    # loop rows
        text = ax.text(j, i, monthly_content.to_numpy()[i, j],
                       ha="center", va="center", color="grey")

plt.xticks(np.arange(0.5, len(monthly_content.columns), 1), monthly_content.columns, fontsize=3, fontfamily='serif', ha="right", rotation=315)
plt.yticks(np.arange(0.5, len(monthly_content.index), 1), monthly_content.index, fontsize=3, fontfamily='serif', va='bottom', rotation=315)

plt.title('Netflix Contents Update', fontsize=12, fontweight='bold', position=(0.20, 1.0+0.02))

ax.tick_params(labelsize=8) 
#cbar.ax.minorticks_on()
fig.colorbar(im, ax=ax, label='Monthly Update')

plt.show()
No description has been provided for this image

TV Rating (Parental Guideline)¶

In [10]:
netflix_movies['rating'].unique()
Out[10]:
array(['TV-MA', 'R', 'PG-13', 'TV-14', 'TV-PG', 'NR', 'TV-G', 'TV-Y', nan,
       'PG', 'G', 'TV-Y7', 'NC-17', 'TV-Y7-FV', 'UR'], dtype=object)
In [11]:
plt.figure(figsize=(12,10))
sns.set(style="darkgrid")
ax = sns.countplot(x="rating", data=netflix_movies, palette="Set2", hue='rating',
                   order=netflix_movies['rating'].value_counts().index[0:15])
No description has been provided for this image

IMDb rating and movie DF¶

In [12]:
imdb_ratings = pd.read_csv('IMDb_ratings.csv',usecols=['weighted_average_vote'])
imdb_titles = pd.read_csv('IMDb_movies.csv', usecols=['title','year','genre'],low_memory=False)
ratings = pd.DataFrame({'Title':imdb_titles.title,
                    'Release Year':imdb_titles.year,
                    'Rating': imdb_ratings.weighted_average_vote,
                    'Genre':imdb_titles.genre})
ratings.drop_duplicates(subset=['Title','Release Year','Rating'], inplace=True)
ratings.shape
Out[12]:
(85852, 4)
In [13]:
ratings.dropna()
joint_data = ratings.merge(netflix_overall,left_on='Title',right_on='title',how='inner')
joint_data = joint_data.sort_values(by='Rating', ascending=False)
In [14]:
joint_data.sample(3)
Out[14]:
Title Release Year Rating Genre show_id type title director cast country date_added release_year rating duration listed_in description
1451 Benji 2018 6.3 Crime, Drama, Family s835 Movie Benji Brandon Camp Gabriel Bateman, Darby Camp, Kiele Sanchez, Je... United Arab Emirates, United States March 16, 2018 2018 TV-PG 87 min Children & Family Movies, Dramas A determined dog comes to the rescue and helps...
2226 Pachamama 2018 6.7 Animation, Adventure, Family s4731 Movie Pachamama Juan Antin Adam Moussamih, Charli Birdgenau, Vlastra Vran... France, Luxembourg, Canada June 7, 2019 2019 PG 71 min Children & Family Movies When a sacred statue is taken from his Andean ...
1572 Aiyyaa 2012 4.4 Comedy, Romance s357 Movie Aiyyaa Sachin Kundalkar Rani Mukerji, Prithviraj Sukumaran, Nirmiti Sa... India July 5, 2020 2012 TV-14 151 min Comedies, International Movies, Music & Musicals An outspoken, imaginative girl from a conserva...

Top rated 10 movies on Netflix¶

In [15]:
top_rated=joint_data[0:10]
fig =px.sunburst( top_rated, path=['title','country'], 
    values='Rating', color='Rating')
fig.show()

Countries with highest rated content.¶

In [16]:
country_count=joint_data['country'].value_counts().sort_values(ascending=False)
country_count=pd.DataFrame(country_count)
topcountries=country_count[0:11]
topcountries
Out[16]:
count
country
United States 799
India 701
United Kingdom 107
Canada 56
Philippines 50
Spain 40
South Korea 36
Indonesia 35
France 33
United Kingdom, United States 31
Australia 30
In [17]:
# Hong Kong people don't know IMDb much
country_count.loc[country_count.index=='Hong Kong']
Out[17]:
count
country
Hong Kong 8

Movies released per year¶

In [18]:
plt.figure(figsize=(12,10))
sns.set(style="darkgrid")
ax = sns.countplot(y="release_year", data=netflix_movies, #palette="Set2", hue='release_year',
                   order=netflix_movies['release_year'].value_counts().index[0:15])
No description has been provided for this image

Film production countries¶

In [19]:
# some movie was filmed and produced in a few countries
netflix_movies['country'].sample(5)
Out[19]:
231                      United States
1675                     United States
7621            Germany, United States
2826            Germany, United States
3249    Germany, United States, Canada
Name: country, dtype: object
In [20]:
countries={}
netflix_movies.loc[netflix_movies.index, ['country']] = netflix_movies['country'].fillna('Unknown')
cou=list(netflix_movies['country'])
for i in cou:
    i=list(i.split(','))
    if len(i)==1:
        if i in list(countries.keys()):
            countries[i]+=1
        else:
            countries[i[0]]=1
    else:
        for j in i:
            if j in list(countries.keys()):
                countries[j]+=1
            else:
                countries[j]=1
In [21]:
countries
Out[21]:
{'Mexico': 1,
 'Singapore': 3,
 'United States': 1,
 'Egypt': 1,
 'India': 1,
 'Thailand': 1,
 'Nigeria': 1,
 'Norway': 2,
 ' Iceland': 4,
 ' United States': 331,
 'United Kingdom': 2,
 'South Korea': 1,
 'Italy': 1,
 'Canada': 3,
 'Indonesia': 1,
 'Romania': 3,
 'Spain': 1,
 'Turkey': 1,
 'Iceland': 1,
 'South Africa': 1,
 ' Nigeria': 5,
 'France': 4,
 ' South Africa': 14,
 'Portugal': 2,
 ' Spain': 39,
 'Unknown': 1,
 'Hong Kong': 1,
 ' China': 54,
 ' Singapore': 8,
 ' Germany': 89,
 'Argentina': 2,
 ' France': 128,
 ' Serbia': 4,
 'Germany': 3,
 'Denmark': 2,
 ' Poland': 9,
 'Poland': 1,
 'Japan': 2,
 'Kenya': 2,
 'New Zealand': 2,
 ' United Kingdom': 126,
 'Pakistan': 1,
 'Australia': 1,
 'China': 1,
 ' Hong Kong': 23,
 'Taiwan': 1,
 ' South Korea': 13,
 ' Japan': 28,
 ' Canada': 111,
 'Netherlands': 1,
 ' Denmark': 12,
 ' Netherlands': 17,
 'Philippines': 1,
 'United Arab Emirates': 2,
 'Brazil': 1,
 'Iran': 1,
 ' Belgium': 68,
 ' Brazil': 7,
 'Israel': 1,
 ' Mexico': 22,
 'Uruguay': 2,
 ' Argentina': 14,
 'Bulgaria': 1,
 ' Chile': 4,
 ' Bulgaria': 4,
 'Colombia': 1,
 ' Algeria': 2,
 ' Egypt': 4,
 'Soviet Union': 1,
 ' India': 32,
 'Sweden': 2,
 'Malaysia': 1,
 'Ireland': 1,
 ' Italy': 25,
 ' Luxembourg': 10,
 'Serbia': 3,
 ' Sweden': 20,
 ' Colombia': 4,
 ' Austria': 3,
 'Peru': 2,
 ' Senegal': 2,
 'Chile': 4,
 ' Switzerland': 10,
 'Ghana': 1,
 ' United Arab Emirates': 15,
 'Saudi Arabia': 3,
 ' Armenia': 1,
 ' Jordan': 7,
 ' Mongolia': 1,
 ' New Zealand': 7,
 ' Philippines': 3,
 ' Uruguay': 5,
 'Namibia': 1,
 ' Finland': 4,
 ' Australia': 28,
 ' Ireland': 8,
 'Lebanon': 2,
 'Belgium': 1,
 ' Indonesia': 3,
 ' Iran': 3,
 ' Qatar': 7,
 'Vietnam': 1,
 'Russia': 3,
 ' Russia': 5,
 ' Israel': 6,
 ' Malta': 2,
 'Kuwait': 1,
 ' Pakistan': 2,
 ' Czech Republic': 10,
 ' Bahamas': 1,
 ' Sri Lanka': 1,
 ' Cayman Islands': 2,
 ' Bangladesh': 1,
 '': 4,
 'Czech Republic': 4,
 'Zimbabwe': 1,
 'Hungary': 1,
 ' Thailand': 4,
 ' Zimbabwe': 2,
 'Finland': 3,
 ' Norway': 9,
 ' Latvia': 1,
 ' Liechtenstein': 1,
 'Venezuela': 2,
 ' Peru': 4,
 ' Morocco': 6,
 ' Cambodia': 3,
 'Cambodia': 1,
 ' Albania': 1,
 ' Nicaragua': 1,
 ' Lebanon': 5,
 ' Greece': 7,
 ' Turkey': 2,
 ' Croatia': 2,
 ' Guatemala': 1,
 'West Germany': 1,
 'Slovenia': 1,
 'Switzerland': 2,
 ' Dominican Republic': 1,
 ' Portugal': 2,
 'Austria': 1,
 'Bangladesh': 1,
 ' Nepal': 2,
 ' Taiwan': 3,
 ' Samoa': 1,
 ' West Germany': 2,
 ' Bermuda': 1,
 ' Ecuador': 1,
 'Georgia': 1,
 ' Botswana': 1,
 ' Iraq': 2,
 ' Hungary': 4,
 ' Kenya': 2,
 ' Vatican City': 1,
 ' Malaysia': 2,
 ' Angola': 1,
 'Guatemala': 1,
 ' Soviet Union': 2,
 'Jamaica': 1,
 ' Kazakhstan': 1,
 ' Malawi': 1,
 ' Romania': 2,
 'Greece': 1,
 ' Slovakia': 1,
 ' Lithuania': 1,
 ' Afghanistan': 1,
 'Paraguay': 1,
 'Somalia': 1,
 ' Sudan': 1,
 ' Panama': 1,
 ' Slovenia': 2,
 ' Venezuela': 1,
 ' Namibia': 1,
 ' Uganda': 1,
 ' East Germany': 1,
 ' Ukraine': 1,
 'Croatia': 1,
 ' Montenegro': 1}
In [22]:
countries_fin = {}
for country,no in countries.items():
    country=country.replace(' ','')
    if country in list(countries_fin.keys()):
        countries_fin[country]+=no
    else:
        countries_fin[country]=no
        
countries_fin={k: v for k, v in sorted(countries_fin.items(), key=lambda item: item[1], reverse= True)}
In [23]:
countries_fin
Out[23]:
{'UnitedStates': 332,
 'France': 132,
 'UnitedKingdom': 128,
 'Canada': 114,
 'Germany': 92,
 'Belgium': 69,
 'China': 55,
 'Spain': 40,
 'India': 33,
 'Japan': 30,
 'Australia': 29,
 'Italy': 26,
 'HongKong': 24,
 'Mexico': 23,
 'Sweden': 22,
 'Netherlands': 18,
 'UnitedArabEmirates': 17,
 'Argentina': 16,
 'SouthAfrica': 15,
 'SouthKorea': 14,
 'Denmark': 14,
 'CzechRepublic': 14,
 'Switzerland': 12,
 'Singapore': 11,
 'Norway': 11,
 'Poland': 10,
 'Luxembourg': 10,
 'NewZealand': 9,
 'Ireland': 9,
 'Brazil': 8,
 'Chile': 8,
 'Russia': 8,
 'Greece': 8,
 'Serbia': 7,
 'Israel': 7,
 'Uruguay': 7,
 'Jordan': 7,
 'Finland': 7,
 'Lebanon': 7,
 'Qatar': 7,
 'Nigeria': 6,
 'Peru': 6,
 'Morocco': 6,
 'Egypt': 5,
 'Thailand': 5,
 'Iceland': 5,
 'Romania': 5,
 'Bulgaria': 5,
 'Colombia': 5,
 'Hungary': 5,
 'Indonesia': 4,
 'Portugal': 4,
 'Kenya': 4,
 'Taiwan': 4,
 'Philippines': 4,
 'Iran': 4,
 'Austria': 4,
 '': 4,
 'Cambodia': 4,
 'Turkey': 3,
 'Pakistan': 3,
 'SovietUnion': 3,
 'Malaysia': 3,
 'SaudiArabia': 3,
 'Zimbabwe': 3,
 'Venezuela': 3,
 'Croatia': 3,
 'WestGermany': 3,
 'Slovenia': 3,
 'Algeria': 2,
 'Senegal': 2,
 'Namibia': 2,
 'Malta': 2,
 'CaymanIslands': 2,
 'Bangladesh': 2,
 'Guatemala': 2,
 'Nepal': 2,
 'Iraq': 2,
 'Unknown': 1,
 'Ghana': 1,
 'Armenia': 1,
 'Mongolia': 1,
 'Vietnam': 1,
 'Kuwait': 1,
 'Bahamas': 1,
 'SriLanka': 1,
 'Latvia': 1,
 'Liechtenstein': 1,
 'Albania': 1,
 'Nicaragua': 1,
 'DominicanRepublic': 1,
 'Samoa': 1,
 'Bermuda': 1,
 'Ecuador': 1,
 'Georgia': 1,
 'Botswana': 1,
 'VaticanCity': 1,
 'Angola': 1,
 'Jamaica': 1,
 'Kazakhstan': 1,
 'Malawi': 1,
 'Slovakia': 1,
 'Lithuania': 1,
 'Afghanistan': 1,
 'Paraguay': 1,
 'Somalia': 1,
 'Sudan': 1,
 'Panama': 1,
 'Uganda': 1,
 'EastGermany': 1,
 'Ukraine': 1,
 'Montenegro': 1}

Top 10 Movie Production Countries¶

In [24]:
plt.figure(figsize=(8,8))
ax = sns.barplot(x=list(countries_fin.keys())[0:10], y=list(countries_fin.values())[0:10])
ax.xaxis.set_ticks(list(countries_fin.keys())[0:10])
ax.set_xticklabels(list(countries_fin.keys())[0:10], rotation = 90)
ax.set_title('Top 10 Movie Production Countries', fontsize=20)
#color = ['lightblue', 'lightgrey']
Out[24]:
Text(0.5, 1.0, 'Top 10 Movie Production Countries')
No description has been provided for this image

Duration of movie¶

In [25]:
netflix_movies['duration']
Out[25]:
1        93 min
2        78 min
3        80 min
4       123 min
6        95 min
         ...   
7781     88 min
7782     99 min
7783    111 min
7784     44 min
7786     90 min
Name: duration, Length: 5377, dtype: object
In [26]:
netflix_movies.loc[netflix_movies.index,['duration']] = netflix_movies['duration'].map(
                                                            lambda x: int(x.split("min")[0])) 
netflix_movies['duration']
Out[26]:
1        93
2        78
3        80
4       123
6        95
       ... 
7781     88
7782     99
7783    111
7784     44
7786     90
Name: duration, Length: 5377, dtype: object
In [27]:
sns.set(style="darkgrid")
sns.kdeplot(data=netflix_movies['duration'], fill=True)
Out[27]:
<Axes: xlabel='duration', ylabel='Density'>
No description has been provided for this image
In [ ]:
In [28]:
from collections import Counter

genres=list(netflix_movies['listed_in'])
gen=[]

for i in genres:
    i=list(i.split(','))
    for j in i:
        gen.append(j.replace(' ',""))
g=Counter(gen)
g={k: v for k, v in sorted(g.items(), key=lambda item: item[1], reverse= True)}

fig, ax = plt.subplots(figsize=(15,8))

fig = plt.figure(figsize = (10, 10))
x=list(g.keys())
y=list(g.values())
ax.vlines(x, ymin=0, ymax=y, color='green')
ax.plot(x,y, "o", color='maroon')
ax.xaxis.set_ticks(list(g.keys()))
ax.set_xticklabels(x, rotation=45, ha='right')
ax.set_ylabel("Count of movies")
# set a title
ax.set_title("Genres")
Out[28]:
Text(0.5, 1.0, 'Genres')
No description has been provided for this image 
<Figure size 1000x1000 with 0 Axes>

Analysis of TV SERIES on Netflix¶

In [29]:
countries1={}
netflix_shows.loc[netflix_shows.index,['country']]=netflix_shows['country'].fillna('Unknown')
cou1=list(netflix_shows['country'])
for i in cou1:
    #print(i)
    i=list(i.split(','))
    if len(i)==1:
        if i in list(countries1.keys()):
            countries1[i]+=1
        else:
            countries1[i[0]]=1
    else:
        for j in i:
            if j in list(countries1.keys()):
                countries1[j]+=1
            else:
                countries1[j]=1
In [30]:
countries_fin1={}
for country,no in countries1.items():
    country=country.replace(' ','')
    if country in list(countries_fin1.keys()):
        countries_fin1[country]+=no
    else:
        countries_fin1[country]=no
        
countries_fin1={k: v for k, v in sorted(countries_fin1.items(), key=lambda item: item[1], reverse= True)}
In [31]:
plt.figure(figsize=(15,15))
plt.title("Content creating countries")
sns.barplot(y=list(countries_fin1.keys()), x=list(countries_fin1.values()))
plt.ylabel("Arrival delay (in minutes)")
Out[31]:
Text(0, 0.5, 'Arrival delay (in minutes)')
No description has been provided for this image

TV shows with largest number of seasons¶

In [32]:
features=['title','duration']
durations = netflix_shows[features]
durations.loc[durations.index,['no_of_seasons']]=durations['duration'].str.replace(' Season','')
durations.loc[durations.index,['no_of_seasons']]=durations['no_of_seasons'].str.replace('s','')
durations.loc[durations.index,['no_of_seasons']]=durations['no_of_seasons'].astype(str).astype(int)
In [33]:
t=['title','no_of_seasons']
top=durations[t]
top=top.sort_values(by='no_of_seasons', ascending=False)
top20=top[0:20]
top20.plot(kind='bar',x='title',y='no_of_seasons', color='blue',figsize=(15,10))
Out[33]:
<Axes: xlabel='title'>
No description has been provided for this image

Lowest number of seasons¶

In [34]:
bottom=top.sort_values(by='no_of_seasons')
bottom=bottom[20:50]
In [35]:
bottom
Out[35]:
title no_of_seasons
5101 Rapture 1
5186 Revolting Rhymes 1
3340 Kevin Hart: Don’t F**k This Up 1
3386 Killer Inside: The Mind of Aaron Hernandez 1
3379 Kill la Kill 1
1882 Drug Squad: Costa del Sol 1
3370 Kid-E-Cats 1
3368 Kicko & Super Speedo 1
1883 Drugs, Inc. 1
3360 Khotey Sikkey 1
3355 Khelti Hai Zindagi Aankh Micholi 1
3354 Khan: No. 1 Crime Hunter 1
3352 Khaani 1
1890 Dueños del paraíso 1
3472 Kuroko's Basketball 1
3463 Kulipari: Dream Walker 1
3462 Kulipari: An Army of Frogs 1
3454 Krishna Balram 1
3126 Japanese Style Originator 1
3125 Japan Sinks: 2020 1
1947 El Cartel 2 1
3119 Jamtara - Sabka Number Ayega 1
3118 James Acaster: Repertoire 1
3113 Jailbirds 1
1949 El Chavo 1
3098 Jack Taylor 1
3090 J-Style Trip 1
3088 Izzy's Koala World 1
3083 Itaewon Class 1
3147 Jenni Rivera: Mariposa de Barrio 1

Content in Hong Kong¶

In [36]:
netflix_hk = netflix_overall[netflix_overall['country']=='Hong Kong']
nfhk = netflix_hk.dropna()

fig = px.treemap(nfhk, path=['country','director'],
                  color='director', hover_data=['director','title'],
                 color_continuous_scale='Purples')
fig.show()
In [37]:
newest_HK_series = netflix_hk.sort_values(by='release_year', ascending=False)[0:20]
newest_HK_series
Out[37]:
show_id type title director cast country date_added release_year rating duration listed_in description
187 s188 Movie A Home with A View Herman Yau Francis Chun-Yu Ng, Louis Koo, Anita Yuen, Tat... Hong Kong April 30, 2019 2019 TV-MA 92 min Comedies, International Movies When a neighbor blocks their view of the city ...
5528 s5529 TV Show Sexy Central NaN Jeana Ho Pui-yu, Joyce Cheng, Ava Liu, Shiga L... Hong Kong July 20, 2019 2019 TV-MA 1 Season International TV Shows, Romantic TV Shows, TV ... In the bustling center of Hong Kong, five youn...
7497 s7498 Movie We Are Legends Daniel Yee Heng Chan Lam Yiu-sing, Ma Chi Wai, Wiyona Yeung, Eric K... Hong Kong June 1, 2019 2019 TV-14 109 min Action & Adventure, International Movies, Spor... Raised in a boxing gym, two orphaned brothers ...
2797 s2798 TV Show Hong Kong West Side Stories NaN Louis Cheung, Myolie Wu, Justin Cheung, Brian ... Hong Kong April 30, 2019 2018 TV-MA 1 Season International TV Shows, TV Comedies, TV Dramas The intimate lives of young men and women from...
4566 s4567 TV Show OCTB NaN Jordan Chan, Justin Cheung, Kwok-Kwan Chan, Sa... Hong Kong February 5, 2018 2017 TV-14 1 Season Crime TV Shows, International TV Shows, TV Dramas An undercover detective crosses paths with fam...
3513 s3514 Movie Lady Bloodfight Chris Nahon Amy Johnston, Muriel Hofmann, Jenny Wu, Kathy ... Hong Kong August 5, 2017 2016 R 101 min Action & Adventure An American travels to Hong Kong in hopes of w...
3841 s3842 Movie Mad World Chun Wong Shawn Yue, Eric Tsang, Elaine Jin, Charmaine Fong Hong Kong September 20, 2018 2016 TV-MA 102 min Dramas, International Movies A stockbroker struggling with bipolar disorder...
1876 s1877 Movie Drink Drank Drunk Yeung Yat-Tak Carlos Chan, Ken Hung, Deep Ng, Michelle Wai, ... Hong Kong August 24, 2019 2016 TV-MA 92 min Comedies, International Movies Too hungover to report as a witness in a crimi...
7521 s7522 Movie Weeds on Fire Chi Fat Chan Liu Kai Chi, Lam Yiu-sing, Tony Tsz-Tung Wu, P... Hong Kong September 27, 2018 2016 TV-MA 95 min Dramas, Independent Movies, International Movies In 1980s Hong Kong, a school principal forms a...
6021 s6022 Movie Ten Years Jevons Au, Zune Kwok, Chow Kwun-wai, Ng Ka-Leu... Wong Jing, Lau Ho-Chi, Leung Kin-Ping Hong Kong February 12, 2019 2015 TV-MA 102 min Dramas, Independent Movies, International Movies Five shorts reveal a fictional Hong Kong in 20...
1087 s1088 Movie Break Up 100 Lawrence Cheng Ekin Cheng, Chrissie Chow, Ivana Wong, Jase Ho... Hong Kong December 1, 2018 2014 TV-14 105 min Comedies, International Movies, Romantic Movies After their 99th breakup, a career woman and h...
3996 s3997 Movie May We Chat Philip Yung Irene Wan, Peter Mak, Rainky Wai, Heidi Lee, K... Hong Kong August 26, 2018 2014 TV-MA 99 min Dramas, International Movies A messaging app unites a mute prostitute, an u...
6663 s6664 Movie The Midas Touch Chi Keung Fung Chapman To, Charlene Choi, Gao Yunxiang, Shati... Hong Kong July 21, 2019 2013 TV-14 98 min Comedies, International Movies A debt collector takes over the management of ...
5445 s5446 Movie SDU: Sex Duties Unit Gary Mak Chapman To, Shawn Yue, Matt Chow, Kwok Cheung ... Hong Kong December 1, 2018 2013 TV-MA 96 min Comedies, International Movies Four man-children in an elite tactical unit ga...
1816 s1817 Movie Don't Go Breaking My Heart Johnnie To Louis Koo, Gao Yuanyuan, Daniel Wu Hong Kong December 1, 2018 2011 TV-PG 115 min Comedies, International Movies, Romantic Movies A financial analyst succumbs to a love triangl...
3757 s3758 Movie Love In A Puff Pang Ho-cheung Miriam Chin Wah Yeung, Shawn Yue, Singh Hartih... Hong Kong August 1, 2018 2010 TV-MA 103 min Comedies, Dramas, International Movies When the Hong Kong government enacts a ban on ...
4620 s4621 Movie Once a Gangster Felix Chong Ekin Cheng, Jordan Chan, Alex Fong, Michelle Y... Hong Kong December 1, 2018 2010 TV-MA 95 min Action & Adventure, Comedies, International Mo... Two former triads are tapped to run in an elec...
3716 s3717 Movie Look for a Star Andrew Lau Wai-Keung Andy Lau, Qi Shu, Zhang Hanyu, Denise Ho, Domi... Hong Kong December 1, 2018 2009 TV-14 117 min Comedies, International Movies, Romantic Movies While keeping his identity secret, a real esta...
423 s424 Movie All's Well, End's Well (2009) Vincent Kok Louis Koo, Sandra Ng Kwan Yue, Raymond Wong, R... Hong Kong November 9, 2018 2009 TV-14 99 min Comedies, International Movies, Romantic Movies Bound by a family rule that forbids him from m...
293 s294 Movie Accident Cheang Pou Soi Louis Koo, Richie Ren, Stanley Fung Sui-Fan, M... Hong Kong December 1, 2018 2009 TV-MA 87 min International Movies, Thrillers A contract killer skilled at staging lethal ac...

Latest release¶

In [38]:
fig = go.Figure(data=[go.Table(header=dict(values=['Title', 'Release Year']),
                 cells=dict(values=[newest_HK_series['title'],newest_HK_series['release_year']]))
                     ])
fig.show()
In [ ]: